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Consensus statement on the credibility assessment of machine learning predictors.

Alessandra Aldieri1, Thiranja Prasad Babarenda Gamage2, Antonino Amedeo La Mattina3,4

  • 1Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24 - 10129 Torino, Italy.

Briefings in Bioinformatics
|March 10, 2025
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Summary

This paper establishes 12 credibility criteria for machine learning (ML) predictors in in silico medicine. It ensures reliable ML tools for healthcare decisions by focusing on causal knowledge and bias reduction.

Keywords:
in silico medicinebias robustnesscausal knowledge assessmenterror quantificationmachine learning credibilityregulatory science

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Area of Science:

  • In silico medicine
  • Biomedical research
  • Artificial intelligence in healthcare

Background:

  • Machine learning (ML) predictors are increasingly used in in silico medicine for estimating complex biological quantities.
  • Ensuring the credibility of these ML predictors is paramount for high-stakes clinical and biomedical decision-making.
  • Existing evaluation frameworks may not fully address the unique challenges posed by ML in biomedical applications.

Purpose of the Study:

  • To present a consensus statement on the theoretical foundation for evaluating the credibility of ML predictors in in silico medicine.
  • To outline 12 key statements guiding the rigorous assessment and deployment of ML tools in healthcare.
  • To propose strategies for ensuring the reliability and applicability of ML predictors, considering causal knowledge and biases.

Main Methods:

  • Development of a consensus statement by experts from the In Silico World Community of Practice.
  • Formulation of 12 foundational statements for ML predictor credibility assessment.
  • Comparative analysis of ML predictors and biophysical models to identify unique challenges.

Main Results:

  • A theoretical framework for ML predictor credibility, emphasizing causal knowledge, error quantification, and bias robustness.
  • Identification of specific challenges in ML predictors related to implicit causal knowledge.
  • Proposed strategies to enhance the reliability and applicability of ML predictors in biomedical contexts.

Conclusions:

  • Rigorous evaluation of ML predictor credibility is essential for safe and effective use in in silico medicine.
  • Incorporating causal reasoning and addressing biases are critical for trustworthy ML applications in healthcare.
  • The proposed framework aims to guide researchers, developers, and regulators in the responsible assessment and deployment of ML tools.